Disruptive technologies for smart cities - Data analytics

This article is a continuation of an article we published in August 2020 - “Disruptive Technologies for Smart Cities – Cloud Computing” for the presentation of the interim results of the ongoing Erasmus+ project Smart technologies by design (Smart by Design). The article is based on the materials produced by the project partners GAIA & DEUSTO.
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Current Status

Data Analytics is the approach that allows companies to analyse the data they generate in their activity enabling them to draw conclusions that affect their business. Better known as Big Data, companies manage this information in order to adopt strategies that will help them to improve their business turnover. Thus, it helps them improve operational efficiency, customer user experience and also allows them to improve their business models. All these data generated by companies in their activity is one of the concerns they have to face today. They should evaluate the importance of this information, what information they will have to store or even what part of all these data they can sell.
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Data analysis means the translation of information into opportunities for companies to take advantage of all these data (Schneider. 2017). This is why, “Data Analytics” is also called as a translator or business generator, because it allows to explore personalised solutions to carry out your projects. At present, information as services is a business model that is expanding wherein increasingly more businesses are seeking to monetise the information they obtain. According to the International Statistical Institute, businesses that use information will see their productivity increase by 430 billion dollars by 2020 in contrast with those that do not use it.
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Existing Platforms

Services offered by platforms related to information analysis is growing along with new solutions in terms of storage capacities as well as processing. Some of the platforms that currently exist are as follows:

• Hadoop
• Gridgain
• HPCC
• Storm
• Spark
• Hive
• Kafka
• Flume

Existing Standards

​The first standard on big data was published in the end of 2015 by the International Telecommunication Union (ITU), hence, there are already international rules and standards. ITU-T Y.3600: provides requisites, capabilities and use cases of cloud computing based big data (Y.BigDatareqts, 2015).

Big Data when merged with Cloud Computing offers the ability to collect, store, analyse, visualise and handle large amounts of data, which cannot be analysed with traditional technologies (Iglesias. A, 2015).
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​Key Applications

When we refer to data analysis, we can differentiate five key applications of such technologies: ​

1. Explore massive data or Big Data management. Information management is assumed to be one of the biggest challenges that the companies will be facing for best decision-making, operations improvement and risk reduction. To obtain a more complete view of customers. The companies have a greater number of information sources about their customers, which they manage to provide better and more personalised services, as well as to predict customer behaviour.

• Increase in security. Such technologies are used in order to prevent attacks by locating anomalies that may occur, by analysing patterns and threats. In this usage type, we can distinguish three applications:

• Improve intelligence and surveillance: with continued real time analysis to find patterns.
• Prevention of attacks: with network traffic analysis to deal with espionage, intrusiveness, cyber attacks…
• Prediction and prevention of cybercrime: by analysing telecommunications and social network data to analyse threats and to act before the criminals.

• Operations Analysis. Helps companies to make operational decisions, increasing their intelligence and efficiency. To do so, they can check the updated information with the different possible systems.
• Increase in data storage. Creation of new data storage structures.

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Expected Evolution Over Time

​The expected evolution is that the data volumes will continue to grow due to the expected increase in the number of networked devices. The future platforms will improve the ways in which data is analysed, while SQL will continue to be the standard, Spark is emerging as a complementary tool which will continue to grow.

New tools will be created to analyse without an analyst, companies such as Microsoft and Salesforce have announced such type of solutions. Programmes such as Kafka and Spark that allow to use these data in real time will also continue to be developed. According to many experts, it is thought that “fast data” and “actionable data” will replace big data. It is also expected that algorithm markets will emerge. Companies will begin to buy algorithms instead of programming them and add their own information (Logicalis, 2016). Although such type of solutions already exist, it is assumed that these will grow multi-fold.
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On the other hand, one of the challenges data analytics platforms will face is privacy, especially since the latest regulations made by the European Commission.
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Expected Standards

With regard to standards, The Big Data Value Association (BDVA) is working to define standards of Big Data priorities and interoperability. The association has a team dedicated to this matter (Task Force 6) that, as of today, has already defined a reference model for Big Data.

​A workshop was held in Brussels in June 2017 to collaborate with other standardisation communities to create a roadmap for the harmonisation of Big Data standards. Representations from ETSI, AIOTI WG3, CEN/CENELEC, OASC, ISO/JTC1/WG9, W3C, OneM2M, Industry 4.0, European Commission, PPP based important Big Data projects among others, participated in the event. Follow-up activities took place in 2017 on the side-lines of the ISO IEC JTC1 WG9 Data Reference Architecture meeting held in Dublin.
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Potential Applications

Information analysis has a large number of potential applications and areas of use (Marr.B, 2016):

• Continue working in customer segmentation
• Optimisation and understanding of business processes
• Monitoring and optimisation of business processes
• Improve public health systems
• Improve sport yields of citizens
• Improvements in science and innovation
• Optimise the performance of machinery of companies
• Improvement in security and support for the fulfilment of law
• Applications in Smart Cities related solutions
• Finance

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Author
KISMC